Philip,
I have
mixed feelings on this issue (filling an AI mind with knowledge from
DB's).
I'd
prefer to start with a tabula rasa AI and have it learn everything via
sensorimotor experience -- and only LATER experiment with feeding DB knowledge
directly into its knowledge-store....
However, for our current software-application work with our partly-done
Novamente, we are in fact loading a bunch of knowledge into the Novamente
software system and doing Novamente-based inference on it using special
narrow-AI-ish control schemata.
So it seems likely that -- once the full Novamente AGI design is
implemented and we *finally* start with experiential learning
experiments -- we will initially experiment with a Novamente that has
pre-loaded knowledge in its brain, and draws on this knowledge as appropriate in
the course of its experiential learning. If the pre-loaded knowledge winds
up not helping, due to its ungrounded nature, then we'll revert to the tabula
rasa approach
-- Ben
G
Presumably this is all very obvious, but from comments Ben has made over a fair length of time, it seems he's very reluctant to fill an AGI's head full of downloaded data/rules of thum or whatever. Ben, the language you use suggests that you'd be happy to start with none of this downloaded stuff. But it seems to me that an new Novamente would struggle really badly, perhaps floundering endlessly in its effort to interpret incoming data unless it's primed to make some good guesses and to have some preset notions of what to do with this incoming data.It seems to me that a new-born Novamente needs to be able to use lots of preset rules related to its first learning environment so that of the data coming in, a very large amount of it already makes sense at some level so that the AGI can apply most of it's brain power to resolving a few very simple ambiguities - like we do when solving a jigsaw puzzle. It seems to me the key learning experience comes from successfully mastering these very minor areas of ambiguity thus starting to build up some personally grounded understanding - which can be added to (exponentially?) as the AGI retests the validity of its understanding based on inherited rules of thumb and as it builds a more and more complex picture of what's around it - at each level gaining mastery through resolving minor ambiguities at the new level of understanding.If this model is right then perhaps it shouldn't matter if the AGI has been given a humungous pile of downloaded data/rules of thumb? It would just call on data in the databanks as these seem to be have some useful connection to the data/rules of thumb that the AGI has mastered. Initially the AGI would understand so little that virtually all of the data in it storages would be just so much noise. It would only be able to work it's way into the data as it mastered some initial concepts and concept labels. So in that sense an infant AGI wouldn't be burdened with having too much downloaded ungrounded data - because most of that data would be efectively invisible to it. Isn't this pretty much like a child that has grown up in a house with a huge library, the contents of which only make sense very slowly as the child builds level after level and area after area of base knowledge?Anyway enough for now. If anyone has time for a babe in the sand box I'd love to know what you think of these musings!Cheers, Philip---------------What Is Thought?by Eric B. Baum (Author)Publisher: MIT Press; (January 1, 2004)ISBN: 0262025485Review: In What Is Thought? Eric Baum proposes a computational explanation of thought. Just as Erwin Schr?ger in his classic 1944 work What Is Life? argued ten years before the discovery of DNA that life must be explainable at a fundamental level by physics and chemistry, Baum contends that the present-day inability of computer science to explain thought and meaning is no reason to doubt there can be such an explanation. Baum argues that the complexity of mind is the outcome of evolution, which has built thought processes that act unlike the standard algorithms of computer science and that to understand the mind we need to understand these thought processes and the evolutionary process that produced them in computational terms. Baum proposes that underlying mind is a complex but compact program that corresponds to the underlying structure of the world. He argues further that the mind is essentially programmed by DNA. We learn more rapidly than computer scientists have so far been able to explain because the DNA code has programmed the mind to deal only with meaningful possibilities. Thus the mind understands by exploiting semantics, or meaning, for the purposes of computation; constraints are built in so that although there are myriad possibilities, only a few make sense. Evolution discovered corresponding subroutines or shortcuts to speed up its processes and to construct creatures whose survival depends on making the right choice quickly. Baum argues that the structure and nature of thought, meaning, sensation, and consciousness therefore arise naturally from the evolution of programs that exploit the compact structure of the world.
To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
